THE EFFECT OF LIQUIDITY AND SOLVENCY ON THE
PROFITABILITY OF COMMERCIAL BANKS IN KENYA
BY:
MBURU RUTH MUTHONI
D63/69164/2013
A RESEARCH PROJECT SUBMITTED IN PARTIAL
FULFILMENT OF THE REQUIREMENTS FOR THE AWARD OF
THE DEGREE OF MASTER OF SCIENCE IN FINANCE, OF THE
UNIVERSITY OF NAIROBI
OCTOBER 2015
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DECLARATION
This research project is my original work and it has not been submitted for any academic
award in any University or institution of higher learning.
Signature: ........................................ Date: ............................................
RUTH MUTHONI MBURU
D63/69164/2013
This research project has been presented for examination with my approval as the
University Supervisor.
Signature: .......................................... Date: ...........................................
MR. HERICK ONDIGO
Lecturer
Department of Finance and Accounting, School of Business
University of Nairobi
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ACKNOWLEDGEMENTS
I thank God for His unceasing love in granting me the opportunity to pursue my MSC
degree and the ability to successfully undertake the research. I also express my sincere
gratitude to my supervisor, Mr. Herick Ondigo, for his invaluable guidance throughout
the research work without which it would have been a rocky road to tread on.
Special thanks to my best friends, Grace Kimotho, Ibra Mwaura and Beryl Atieno,
Boniface Duplex and Fredrick Ombako, for giving me a lot of moral support without
which I could not have completed this research project successfully. Finally, I also
appreciate the University of Nairobi for offering a flexible MSC programme to allow
even for the employed to fulfil their academic dreams.
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DEDICATION
I dedicate this research project to my entire family. To my dad, Simon Mburu, thank you
for being my greatest mentor, my academic star and the beacon that I will always lean
on, it is because of your immense support that I was able to complete my research
project. Your sacrifices paid off. To my mother, Ann Wanjiku, thank you for your
unending encouragement, for believing in me and for being the voice of reason in my
life.
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TABLE OF CONTENTS
DECLARATION............................................................................................................... ii
ACKNOWLEDGEMENTS ............................................................................................ iii
DEDICATION.................................................................................................................. iv
LIST OF FIGURES ....................................................................................................... viii
LIST OF TABLES ......................................................................................................... viii
LIST OF ABBREVIATIONS ......................................................................................... ix
ABSTRACT ....................................................................................................................... x
CHAPTER ONE: INTRODUCTION ............................................................................. 1
1.1 Background of the Study ...................................................................................... 1
1.1.1 Liquidity ........................................................................................................ 2
1.1.2 Solvency ........................................................................................................ 3
1.1.3 Profitability ................................................................................................... 4
1.1.4 Effect of Liquidity and Solvency on Profitability......................................... 5
1.1.5 Commercial Banks in Kenya ........................................................................ 6
1.2. Research Problem ................................................................................................. 7
1.3. Research Objective ............................................................................................... 9
1.4. Value of the Study ................................................................................................ 9
CHAPTER TWO: LITERATURE REVIEW .............................................................. 11
2.1 Introduction ........................................................................................................ 11
2.2 Theoretical Review ............................................................................................ 11
2.2.1 Baumol Model Theory ................................................................................ 11
2.2.2 The Miller-Orr Model ................................................................................. 12
2.2.3 Liquidity Preference Theory ....................................................................... 13
2.2.4 Shiftability Theory ...................................................................................... 14
2.3 Determinants of Profitability in Commercial Banks .......................................... 14
2.3.1 Liquidity ...................................................................................................... 15
2.3.2 Solvency ...................................................................................................... 15
2.3.3 Asset Quality ............................................................................................... 16
2.3.4 Size of the Bank .......................................................................................... 17
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2.3.5 Growth ........................................................................................................ 17
2.4 Empirical Literature Review .............................................................................. 18
2.4.1 International Evidence ................................................................................ 18
2.4.2 Local Evidence............................................................................................ 21
2.5 Summary of Literature Review .......................................................................... 23
CHAPTER THREE: RESEARCH METHODOLOGY……………………………..25
3.1 Introduction ........................................................................................................ 25
3.2 Research Design ................................................................................................. 25
3.3 Population........................................................................................................... 25
3.4 Data Collection ................................................................................................... 26
3.5 Data Analysis ..................................................................................................... 26
3.5.1 The Analytical Model ................................................................................. 27
3.5.2 Operationalization of the Variables ............................................................ 28
3.5.3 Tests of Significance ................................................................................... 28
CHAPTER FOUR: DATA ANALYSIS, FINDINGS AND INTERPRETATIONS . 30
4.1 Introduction ........................................................................................................ 30
4.2 Descriptive Statistics .......................................................................................... 30
4.3 Inferential Statistics ............................................................................................ 31
4.3.1 Correlation Analysis ................................................................................... 31
4.3.2 Regression Analysis .................................................................................... 32
4.3.3 Analysis of Variance ................................................................................... 32
4.4 Interpretation of the Findings ............................................................................. 34
CHAPTER FIVE: SUMMARY, CONCLUSION AND RECOMMENDATIONS .. 36
5.1 Introduction ........................................................................................................ 36
5.2 Summary ............................................................................................................ 36
5.3 Conclusion .......................................................................................................... 37
5.4 Recommendations for Policy and Practice......................................................... 38
5.5 Limitations of the Study ..................................................................................... 39
5.6 Suggestions for Further Research ...................................................................... 40
REFERENCES ................................................................................................................ 42
APPENDICES ................................................................................................................. 47
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Appendix I: List of Commercial Banks in Kenya as at 31 December 2014 ............ 47
Appendix II: Final Research Data for Analysis ........................................................ 48
Appendix III: Annual Ratios for variables I ............................................................... 49
Appendix IV: Annual Ratios for variables II .............................................................. 51
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LIST OF FIGURES
Figure 2.1 Cash movements between the two limits………………………………..12
LIST OF TABLES
Table 3.1 Operationalisation of variables……………………………………….....28
Table 4.1 Descriptive statistics………………………………………………….....30
Table 4.2 Correlation matrix…………………………………………………….....31
Table 4.3 Model summary………………………………………………………....32
Table 4.4 ANOVA………………………………………………………………...32
Table 4.5 Coefficients……………………………………………………………..33
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LIST OF ABBREVIATIONS
AFS Audited Financial Statements
ANOVA Analysis of Variance
BCBS Basel Committee on Banking Supervision
CAMEL Capital Adequacy, Asset Quality, Management Quality, Earnings and
Liquidity
CAR Capital Adequacy Ratio
CBK Central Bank of Kenya
COGS Cost of Goods Sold
COMESA Common Market for Eastern and Southern Africa
CRB Credit Reference Bureau
EBIT Earnings Before Interest and Tax
GDP Gross Domestic Product
KDIC Kenya Deposit Insurance Corporation
MFB Micro-Finance Bank
MFC Mortgage Finance Company
MFI Micro-Finance Institutions
NPM Net Profit Margin
NSE Nairobi Securities Exchange
OLS Ordinary Least Squares
ROA Return on Assets
ROE Return on Equity
SACCO Savings and Credit Cooperatives
SCP Structure Conduct Performance
SHIELDS Solvency Conditions; Home Economics Conditions; Institutional Quality;
Earnings Conditions; Liquidity Conditions; Default Conditions; and
Systematic Loss
UK United Kingdom
USA United States of America
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ABSTRACT
The primary function of banks is to convert liquid deposits (liabilities) to illiquid assets
such as loans which make them inherently vulnerable to liquidity risk. Lack of liquidity
in bank‟s statement of financial position is an indicator of a liquidity crisis in a banking
system. On the other hand, illiquidity, unless remedied, will give rise to insolvency and
eventually bankruptcy as the business‟s liabilities exceed its assets. The fact that it is
impossible for banks to survive without making profits cannot be overemphasised. This
study sought to examine the effect of liquidity and solvency on the profitability of
Commercial Banks in Kenya.
The study used a descriptive research design. The population of this study comprised the
entire population of all the 43 Commercial Banks in Kenya (Appendix 1) and 42 out of
the 43 Commercial Banks formed the sample. Five year secondary data was collected
from 2010 to 2014 for the banks from their annual reports. Data was analysed using
descriptive, correlation and regression analyses.
The regression results showed that the model explained 42.4% of the variance in bank
performance. The ANOVA results showed that the model was statistically significant at
1% level of significance. The study found that both liquidity and solvency had negative
but insignificant effects on the performance of banks in Kenya. Further, the study found
that asset quality had a negative but insignificant effect on bank performance while
growth had a positive but insignificant effect on the bank performance in Kenya. The
results showed that bank size had a positive and significant effect on bank performance.
The study concludes that the performance of Commercial Banks in Kenya is not
influenced by both liquidity and solvency.
The study recommends that the management of Commercial Banks in Kenya should take
note of the fact that while the liquidity and solvency levels of banks were not found to
influence bank performance, it is important to keep them at manageable levels in relation
to the industry. The study also recommends that bank managers should take note of the
fact that the size of the banks influences their performance. As such, Commercial Banks
should strive to have higher asset base in the industry in order to record better
performance in terms of profitability. The study further recommends that since growth in
bank revenues may have a positive impact on the performance of banks in Kenya, banks
should focus on improving their revenue sources in order to record better performance.
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CHAPTER ONE: INTRODUCTION
1.1 Background of the Study
In the recent past, there has been an increased interest in the performance of Commercial
Banks following the financial turmoil of 2007 that revealed the importance of liquidity and
solvency for the smooth running of the global financial system. The uncertainty that was
inherent in the financial crisis resulted to banks being unable to cover their obligations due to
shortage in cash. As a result, in the interest of broader financial stability, substantial amounts
of liquidity were provided by authorities in many countries, including Canada and the United
States (Longworth, 2010; Bernanke, 2008).
There are a total of 43 Commercial Banks in Kenya at the moment. The banking sector has
played a critical role in financing economic activities in the various market segments and in
order to do so, they need to remain profitable (Ongera and Kusa, 2013). Additionally, the
banking sector in Kenya has been characterised by stiff competition from within and from
other financial institutions such as the Micro-Finance Institutions (MFIs), Savings and Credit
Cooperatives (SACCOs) and Mortgage institutions. There is therefore need for Commercial
Banks to remain financially stable in order to remain relevant and competitive in the financial
market. The financial stability can only be achieved if the banks are profitable and this study
sought to understand the effect that liquidity and solvency have on the profitability of
Commercial Banks in Kenya.
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1.1.1 Liquidity
In 2000, Basel Committee on Banking Supervision defined liquidity as the ability to fund
increases in assets and meet obligations as they come due (BCBS, February 2000). A more
general definition was introduced in 2008 defining liquidity as the ability of a bank to fund
increases in assets and meet obligations as they come due, without incurring unacceptable
losses (BCBS, September 2008). Liquidity refers to the speed and certainty with which an
asset can be converted back into money (cash, income) whenever the asset holder desires.
Liquid assets are those that can be converted into cash quickly in order to meet maturing
financial obligations. Cash, short-term marketable securities and central bank reserves are
examples of liquid assets with cash being the most liquid of all. A bank must have sufficient
liquid assets to meet its near term obligations such as withdrawals by depositors. A financial
institution that has a higher investment in current assets has a higher liquidity level.
The key ratios used to measure liquidity are the current ratio and the quick ratio. Current ratio
is calculated by dividing the total current assets by total current liabilities whereas the quick
ratio is computed by deducting inventories from current assets and dividing the result by
current liabilities. The higher the current ratio and the quick ratio, the better the financial
position of the business. However, critics have argued that a very high current ratio might be
an indicator that a company is sitting around with a lot of cash as it lacks the managerial
acumen to put those resources to work.
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1.1.2 Solvency
Solvency is the ability of a financial institution to meet its obligations in the event of cessation
of activity or liquidation. It refers to a company‟s long run financial viability and its ability to
cover long-term obligations. A bank is considered solvent if the total assets exceed total
liabilities. If the total assets are lower than total liabilities, the bank faces an insolvency risk
and is said to be „technically insolvent‟. Insolvency risk shows the probability of default of a
representative bank. The solvency problem tends to be more long-term than the previously
described liquidity issue and historically, banks have always held on to funds and stopped
lending when there is a solvency crisis (Mason, 2009). Financial ratios that measure solvency
include total debt to total capital, total debt to equity capital, long-term debt to equity capital
and short-term debt to equity ratios.
Liquidity is somehow short term solvency. Mehdi and Mohammed (2014) opined that the
difference between liquidity and solvency lies in the fact that a liquid bank does not imply
that it is solvent while a solvent bank does not imply that it is liquid. Goodhart (2008)
remarked that an illiquid bank can rapidly become insolvent, and an insolvent bank illiquid.
Thus, liquidity and solvency are the heavenly twins of banking, frequently indistinguishable.
Both liquidity and solvency relate to default. A liquidity crisis will occur when a company has
temporary cash flow problems but a solvency crisis is when a company has debts that it can‟t
honor through its assets such that even if it was to sell its total assets, it would still be unable
to settle its debts. Illiquidity is a sufficient but not a necessary condition for default. Following
Matz (2001): “then, the bank‟s liquidity provides some amount of survival time during which
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the crisis is resolved or not. Ultimately, capital must cover the losses. But in the meantime,
sufficient liquidity can be the single most decisive factor in a bank‟s ability to survive a
crisis.”
1.1.3 Profitability
Profitability is a measure of the net revenue and expenses. Revenue refers to increases in
owners‟ equity resulting from sale of goods or performance of services in the ordinary course
of business. It consists of cash, or a promise to receive cash in the future (accounts
receivable). Expenses are decreases in owners‟ equity resulting from the costs incurred in
order to earn revenue. They may involve immediate cash payment or promises to pay in the
future. Profitability is a key measure of a successful business. A business that is not
profitable may not survive while a business that is highly profitable has the ability to reward
its owners with large returns on their investment (Kithii, 2008).
Profitability is the ultimate objective of all business ventures, both in the short-run and in the
long-run. A business has to remain profitable in order to withstand negative shocks and
survive in the long-run. Therefore, it is important to measure current and past profitability as
well as project future profitability. Gross profit is the sales less direct cost of goods (or
services) sold (COGS) while net profit is determined by deducting a company‟s selling,
general and administrative expenses, depreciation costs and taxes from its revenue and any
other income.
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The measures of profitability include Return on Assets (ROA) which is calculated by dividing
a company‟s net income by the average total assets, Return on Equity (ROE), determined by
dividing net income by the average shareholder‟s equity and Net Profit Margin (NPM)
computed by dividing net income by revenues.
1.1.4 Effect of Liquidity and Solvency on Profitability
According to Hirigoyen (1985) the relationship between liquidity and profitability could
become positive over the medium and long run, in the sense that a low liquidity would result
in a lower profitability due to greater need for loans, and low profitability would not generate
sufficient cash flows, thus forming a viscous cycle. In a study done to determine the impact
of liquidity and solvency on the profitability of chemical firms in Pakistan, the researchers
postulated that liquidity has a positive relationship with profitability whereas solvency has an
indirect relationship with the profitability of the chemical firms, Waqas and Mobeen (2014).
Konadu (2009) found no positive relationship between liquidity trend and profitability of
banks in Ghana and concluded that there is a negative relationship between liquidity and
profitability in the Ghana banking sector. Li (2007) found that the result for liquidity on
profitability is mixed and not significant which indicates that conclusion about the impact of
liquidity remains questionable and further research is needed.
Referring to the studies above, the outcomes concerning the effect of liquidity on profitability
of companies have been mixed. It is however expected that there exists a positive relationship
between liquidity and profitability of Commercial Banks in Kenya, at least in the long run.
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1.1.5 Commercial Banks in Kenya
A Commercial Bank is a financial institution primarily engaged in deposit and lending
activities to private and corporate clients in wholesale and retail banking. Banks dominate the
financial sector in Kenya (Kiganda, 2014) and as such the process of financial intermediation
in the country depends heavily on Commercial Banks. In Kenya, Commercial Banks are
licensed and regulated pursuant to the provisions of the Banking Act and the Regulations and
Prudential guidelines issued by the CBK. Commercial Banks listed at the NSE are also
regulated by the Capital Markets Act 2000 cap 485A (amended 2013) and Central Depository
Act 4 of 2000 (amended 2013).
As at 31 December 2014, the banking sector comprised of the Central Bank of Kenya, as the
regulatory authority, 44 banking institutions (43 Commercial Banks and 1 mortgage finance
company - MFC), 7 representative offices of foreign banks, 9 Microfinance Banks (MFBs), 2
CRBs and 101 forex bureaus. Out of the 44 banking institutions, 30 locally owned banks
comprise 3 with public shareholding and 27 privately owned while 14 are foreign. The foreign
owned financial institutions comprise of 10 locally incorporated foreign banks and 4 branches
of foreign incorporated banks.
The CBK adopted the CAMEL rating system in assessing the soundness of Commercial
Banks in the year 2000. The institutions rated strong, satisfactory and fair in December 2014
were 22, 16 and 5 respectively. In 2012, 2013 and 2014, the banking sector was on overall
rated strong. In 2014, the CBK continued to implement the COMESA Financial System
Stability Assessment Framework. The assessment framework is used to assess the financial
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stability of financial institutions over time and it is a comprehensive and structured Rating
System abbreviated as „‟SHIELDS‟‟ which stands for Solvency Conditions; Home Economic
Conditions; Institutional Quality; Earnings Conditions; Liquidity Conditions; Default
Conditions; and Systematic Loss.
The bank annual supervision annual report 2014 stated that the Kenyan banking sector
recorded improved performance in 2014 with the total net assets and customer deposits rising
by 18.5 per cent and 18.65 per cent respectively. The expanded asset base was driven by a
higher demand for credit in 2014 as compared to 2013 while the rise in deposits resulted from
increased deposit mobilization by banks as they expanded their outreach and service networks
to tap unserved segments of the market. For the 12 months period ended 31 December 2014,
the banking sector‟s liquidity ratio stood at 37.7% (2013: 38.6%). The major contributing
factor to the decline in liquidity ratio is the increased lending in 2014 as evidenced by the
increase in loans to deposits ratio from 81.6% in 2013 to 83.1% over the same period. It is
worth noting that the liquidity ratio in 2014 was way above the statutory minimum of 20%.
1.2. Research Problem
The basic goals of a company‟s existence are to maximise shareholders‟ wealth and generate
profits. According to Mehdi and Mohammed (2014), the primary function of banks is to
convert liquid deposits (liabilities) to illiquid assets such as loans which make them inherently
vulnerable to liquidity risk. Bank of Canada (2010) in its working paper, “The impact of
liquidity on bank profitability in Canada” observed that liquidity was an instrumental factor
during the 2008-2009 financial crises. Lack of liquidity in bank‟s statement of financial
8
position is an indicator of a liquidity crisis in a banking system. Liquidity management is
therefore an important objective for all companies since illiquidity may lead to insolvency,
Goodhart (2008) and poor financial performance. On the other hand, illiquidity, unless
remedied, will give rise to insolvency and eventually bankruptcy as the business‟s liabilities
exceed its assets. The fact that it is impossible for banks to survive without making profits
cannot be overemphasised.
Commercial Banks are required by the CBK to maintain a minimum liquidity ratio of 20
percent. The CBK annual supervision report 2014 showed that all the banks met the 20
percent liquidity requirement. In a country where the financial sector plays an important role
in the economy, a failure in the sector would have negative multiple adverse effects.
Following Ongore and Kusa (2013): “any bankruptcy that could happen in the banking sector
has a contagion effect that can lead to bank runs, crises and bring overall financial crisis and
economic tribulations.” Locally, limited studies have been done on the internal factors that
affect the profitability of banks and these studies have not satisfactorily stressed the effect that
liquidity and solvency has on banks‟ profitability.
Studies on the performance of banking industry include Ongore and Kusa (2013) who studied
determinants of financial performance of Commercial Banks in Kenya; Kiganda (2014)
examined the effect of macroeconomic factors on Commercial Banks profitability in Kenya:
Case of Equity Bank Limited. Although Olweny and Shipho (2011) studied the effects of
banking specific factors on the profitability of Commercial Banks in Kenya, the variables
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used; capital adequacy, asset quality, liquidity, operational cost, efficiency and income
diversification in the study were not exhaustive.
Both global and local studies on the relationship have found mixed results. Dang (2011) found
a positive relationship between liquidity and bank‟s profitability while Ongore and Kusa
(2013) reported insignificant relationship between liquidity and profitability of banks. To the
knowledge of the researcher, no specific study has been carried out in Kenya on how liquidity
and solvency affect profitability of Commercial Banks. There is therefore a gap in literature
which the present study seeks to bridge. The following research question will be answered:
what is the effect of liquidity and solvency on the profitability of Commercial Banks in
Kenya?
1.3. Research Objective
To examine the effect of liquidity and solvency on the profitability of Commercial Banks in
Kenya.
1.4. Value of the Study
The findings of this study will have various contributions to the theory and practice of
finance.
The recent global financial crisis stressed on the importance of efficient liquidity management
in the banking systems. Regulators have reacted to this by formulating new liquidity standards
that will ensure soundness, stability and resilience in the financial systems. Bank‟s
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management will use this report as a guide in making capital structure and investment
decisions that would satisfy stakeholders‟ interests with regard to liquidity, solvency and
profitability.
Further, this study will inform policy makers especially the Central Bank of Kenya and
Treasury on how liquidity and solvency affects profitability of banks. This will guide in
policy formulation in both agencies.
Financial consultants will use the results of this study as a guide in advising their clients on
matters relating to liquidity, solvency and profitability. Researchers, students, and other
academicians will also find this study a valuable source of information. Thus, future studies
can be based on the present study especially by taking advantage of the limitations of the
present study and the recommended future research directions.
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CHAPTER TWO: LITERATURE REVIEW
2.1 Introduction
This chapter presents the review of literature. The first section reviews the theoretical
literature where different theories of liquidity, profitability and other relevant theories are
discussed. The second section presents an empirical review where prior studies on liquidity,
solvency and profitability are reviewed. The third section is the summary of the chapter.
2.2 Theoretical Review
This section describes the various theories that have been developed explaining how liquidity
and solvency impacts on profitability of companies. A number of theories are reviewed here
and their relevance to the present study explained. These theories are Baumol Model Theory,
Miller-Orr Model Theory, Liquidity Preference Theory and the Shiftability Theory.
2.2.1 Baumol Model Theory
According to Baumol (1952), Baumol model of cash management enables companies to find
out the optimum level of cash balance to hold under conditions of certainty. It relies on the
trade-off between the liquidity provided by holding money and the interest foregone by
holding one‟s assets in the form of non-interest bearing current accounts. This model is useful
in determining target cash balance.
The Baumol model assumes constant outflow of cash payments and that the firm only
receives cash at the end of a particular period. It also assumes that the opportunity cost of
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holding the cash is known and that cash is held in short-term investments. With the inflows
and outflow patterns determined, then the firm is able to set average cash balance which is the
target cash.
2.2.2 The Miller-Orr Model
The Miller-Orr model was developed by Miller and Orr (2006) and it is used for setting the
target cash balance for a company. The model recognises the fact that cash flows are not
certain and it addresses the limitation of the Baumol model which does not allow cash flows
to fluctuate. To overcome this limitation, the Miller-Orr Model allows for daily cash flow
variations.
The diagram below shows how the model works:
Fig 2.1: Cash movements between the two limits
Source: Brigham and Houston (2007).
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The model sets higher and lower control limits, H and L respectively, as well as a target cash
balance, Z. When the cash balance reaches H, then (H-Z) dollars are transferred from cash to
marketable securities, i.e. the company buys a specified number of sellable securities in order
to reach the desired cash level, i.e. target cash balance. Similarly, when the cash balance hits
L, then (Z-L) dollars are transferred from marketable securities to cash. The factors that
determine the variance between H and L are the transaction cost, the interest rate and the
standard deviation. Lower limits are set by management depending on how much risk of a
cash shortfall the firm is willing to accept and this in turn is dependent on access to
borrowings and on the implications of the cash shortfall.
2.2.3 Liquidity Preference Theory
In macro-economic theory, liquidity preference refers to the demand for money, considered as
liquidity. Keynes (1936) was the first to develop the concept of liquidity in his book The
General Theory of Employment, Interest and Money to explain determination of the interest
rate by the supply and demand for money.
Keynes (1936) identified three motives on why people demand and prefer liquidity: the
transaction motive where companies and individuals hold cash in order to carry out day to day
transactions; the precautionary motive where cash is held to meet unforeseen emergencies; the
speculative motive which is creating the ability for a company to take advantage of special
opportunities that if acted upon quickly will favour the firm.
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2.2.4 Shiftability Theory
This theory was originated in the USA by Moulton (1918). This theory postulates that a
bank‟s liquidity is maintained if it holds assets that could be shifted or sold to other lenders or
investors for cash. Following Moulton: “to attain minimum reserves, relying on maturing bills
is not needed but maintaining quantity of assets which can be shifted to other banks whenever
necessary. It must fulfil the attributes of immediate transferability to others without loss. In
case of general liquidity crisis, bank should maintain liquidity by possessing assets which can
be shifted to the Central Bank”. Thus this theory contends that shiftability, marketability or
transferability of a bank's assets is a basis for ensuring liquidity.
This theory has some elements of truth in that Commercial Banks now accept sound assets
which can be shifted to other banks. For instance, shares, debentures, treasury bills and bills
of exchange of large companies are accepted as liquid assets. However, critics have argued
that the theory rules out the fact that during acute depression, the shares and debentures
cannot be shifted on to other lenders or investors by the banks.
2.3 Determinants of Profitability in Commercial Banks
Generally, a number of factors tend to affect profitability of Commercial Banks as several
other studies have examined and determined. The factors reviewed in this study are liquidity,
solvency, asset quality, size of the bank and growth.
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2.3.1 Liquidity
The Economic Times, (2014) defines Liquidity as “Liquidity means how quickly you can get
your hands on your cash. In simpler terms, liquidity is to get your money whenever you need
it”. It refers to the ability of the bank to fulfil its obligations, mainly of depositors. As such,
liquidity is a prime concern for banks and a short fall in liquidity would result into bank
failure. The most common financial ratios that reflect the liquidity position of a bank are
customer deposit to total asset and total loan to customer deposits. Others are cash to deposit
ratio, Ongore and Kusa (2013).
Dang (2011) asserted that adequate level of liquidity is positively related with bank
profitability. However, Molyneux and Thorton, (1992) and Guru, Staunton and
Balashanmugam, (1999) discovered a negative relationship between the level of liquidity and
profitability; in their analysis, they argued that holding liquid assets tends to reduce income
due to the lower rates of return associated with liquid assets.
2.3.2 Solvency
Solvency is one of the bank specific factors that has an influence on the performance of a
bank. A company whose total liabilities exceed total assets is said to be „‟technically
insolvent.‟‟ A bank can become insolvent if it is unable honour its long term financial
obligations. This means that it may be impossible for the bank to repay its depositors. This
may arise when customers default on their loans for a sustained period of time, a situation
which may result into a bank run. One of the key financial ratios that is used to measure the
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solvency of a bank is ratio of debt to equity. The ratio indicates the degree of financial
leverage being used by the bank and includes both short term and long term debt.
On 24 August 2015, the CBK approved the insolvency of Dubai Bank Kenya and ordered the
closure of the Bank due its inability to pay its debts and for flouting regulations. The Bank
will be liquidated on the recommendation of the KDIC that was appointed by the CBK as a
receiver for Dubai Bank Kenya on 14 August 2015 in view of its serious liquidity and capital
deficiencies. The recommendation was premised on KDIC‟s review of Dubai Bank Kenya
which indicated that the magnitude of weaknesses in the bank left liquidation as the only
feasible option. The CBK said that such violations and indebtedness were detrimental to the
interests of its depositors, creditors and public (the CBK website).
Sufian (2011) investigated the determinants of profitability of the Korean banking sector in
which bank specific and macro-economic factors were evaluated. The results revealed that
solvency and liquidity level, credit risk, diversification, industry concentration and business
risk have a significant effect on the profitability of banks. Omari, Warrad and Al-Nimer
(2013) concluded that solvency has a significant relationship with profitability of firms in the
Jordanian Industrial Sector.
2.3.3 Asset Quality
Credit portfolio is an important class of assets for a bank since loan is the major asset of
Commercial Banks from which income is generated. The quality of the loan portfolio has a
direct bearing on the profitability of banks. The highest risk facing a bank is the losses derived
from delinquent loans (Dang, 2011).
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Thus, non-performing loan ratios are the best proxies for asset quality and all Commercial
Banks should strive to keep the amount of non-performing loans to a low level. A study by
Sangmi and Nazir (2010) confirmed that a lower ratio of non-performing loans to total loans
translated to a higher level of profitability in banks. According to Kosmidou (2008), poor
asset quality can have adverse impact on bank profitability, reducing interest income revenue,
and by increasing the provisions cost.
2.3.4 Size of the Bank
Ordinarily, total asset is used as a measure for bank size. There is a general consensus in
literature that a larger size should allow a bank to obtain economies of scale. Economies of
scale will reduce the cost of gathering and processing information so that a positive effect of
bank size is associated with profitability.
Berger, Akhavein and Humphrey (1997) and Smirlock (1985) found a positive and significant
relationship between size of the bank and profitability. Haslem (1968), Short (1979), Bourke
(1989) and Goddard, Molyneux and Wilson (2004) have all linked bank size to capital ratios,
which they claim to be positively related to size. These outcomes confirm that there is a direct
relationship between size and profitability and this is especially so in the case of small to
medium-sized banks.
2.3.5 Growth
Bank growth indicator is given by natural logarithm of total bank assets. Boyd and Runkle
(1993) established a significant inverse relationship between size and return on assets in U.S
18
banks from 1971 to 1990 and positive relationship between financial leverage and size of
banks.
Akhavein, et al., (1997) showed that banks experience some diseconomies of scale to
negatively affect performance. Goddard, et al., (2004), on five European countries, observed
that the growth in bank size could positively influence bank performance. These observations
suggest that the expected impact of bank size on bank profitability could be positive.
2.4 Empirical Literature Review
This section reviews various studies on liquidity, solvency and profitability. Both
international and local studies have been reviewed.
2.4.1 International Evidence
Internationally, a number of studies have been carried out to examine the effect of liquidity
and solvency on profitability.
Bourke (1989) carried out a study to establish the relationship between liquid assets and bank
profitability for 90 banks in Europe, North America and Australia from 1972 to 1981, the
study used econometric framework presented in an equation. The dependent variable,
profitability, was regressed against a non‐linear expression of relative liquid asset holdings, as
well as a set of control variables. From the study a company with low liquidity and high
profitability has to increase its borrowing leading to an increase of the financial costs. The
study emphasized that profitability and solvency are necessary condition for the healthy
19
existence of the company and both are conditioned by the strategy adopted in the medium and
long term.
Graham and Bordeleau (2010) did a study on the impact of liquidity on profitability of Banks
in Canada. The study was aimed at helping to distinguish empirically, whether banks‟
holdings of liquid assets have a significant impact on their profitability. Since liquid assets
such as cash and government securities generally have a relatively low return, holding them
imposes an opportunity cost on a bank. In the model, profitability is regressed as a non-linear
expression of relative liquid asset holdings as well as a set of control variables. The
relationship is a function of the liquid assets ratio, a measure of short-term funding reliance
and general macroeconomic conditions. While controlling for other factors, the paper found
evidence, based on a panel of Canadian and American banks from 1997 to the end of 2009,
that profitability is improved for banks that hold some liquid assets; however, there is a point
at which holding further liquid assets diminishes a bank‟s profitability, all else equal.
Abera (2012) studied factors affecting profitability; an empirical Study on Ethiopian banking
industry. This study examined the bank-specific, industry-specific and macro-economic
factors affecting bank profitability for a total of eight Commercial Banks in Ethiopia,
covering the period of 2000-2011 using a mixed methods research approach by combining
documentary analysis and in-depth interviews. The study noted that despite the findings of the
regression analysis that the impact of liquidity was negligible, liquidity of banks was one of
the major determinants of Ethiopian banks profitability. The study concluded that the impact
20
of Ethiopian banks‟ liquidity on their performance remains ambiguous and further research is
required.
Lartey, Antwi and Boadi (2013) sought to find out the relationship between the liquidity and
the profitability of banks listed on the Ghana Stock Exchange. The study sought to describe
the relationship between the liquidity and the profitability of banks listed on the Ghana Stock
Exchange using a target population of 9 Commercial Banks listed on the Ghana Stock
Exchange and a sample of 7 banks. Purposive sampling technique was used. In conclusion,
both the liquidity and the profitability levels of the listed banks were decreasing within the
period 2005-2010. There was a very weak positive relationship between the liquidity and the
profitability of the listed banks. These findings support Munther and Omari (2013) in the case
of Jordanian banks. When banks hold adequate liquid assets, their profitability would
improve. Adequate liquidity helps the bank minimize liquidity risk and financial crises. The
bank can absorb any possible unforeseen financial position. However, if liquid assets are held
excessively, profitability could diminish because they have no or little interest generating
capacity. The opportunity cost of holding low return assets would eventually outweigh the
benefit of any increase in the bank‟s liquidity resiliency as perceived by funding markets
(Mashhad, 2012).
Omari, Warrad and Al-Nimer (2013) investigated the effect of solvency among Jordanian
Industrial sectors. In this study, solvency was expressed by debt ratio (Debt), and equity ratio
(Equity), and the profitability was expressed by variables including earnings before interest
and tax (EBIT), net profit margin (NPM), return on asset (ROA), and return on equity (ROE),
21
and. for the analysis the multiple regressions covered a period 2008-2011. The study found
that the Mining and Extraction sector and the Glass and Ceramic Industries had the highest
and lowest EBIT, NPM, ROA and ROE respectively. The study concluded that solvency has a
significant relationship with profitability of firms.
2.4.2 Local Evidence
Studies in Kenya have not directly assessed the impact of liquidity and solvency on
profitability but a few studies have been conducted on determinants of banks profitability
coupled with impact of liquidity on profitability of Commercial Banks in Kenya.
Kamoyo (2006) carried out an empirical study on the determinants of liquidity of Commercial
Banks in Kenya. The study involved 30 Commercial Banks operating in Kenya in the period
1995 to 2004. The study applied descriptive statistics, investigative questionnaires and
multiple regression analysis to establish the determinants of liquidity in Commercial Banks.
The results of the study indicated an insignificant negative relationship between profitability
and liquidity.
Ongore and Kusa (2013) studied the determinants of financial performance of Commercial
Banks in Kenya. The authors used linear multiple regression model and generalized Least
Square on panel data to estimate the parameters. This explanatory study is based on secondary
data obtained from published statements of accounts of all Commercial Banks in Kenya,
CBK, IMF and World Bank publications for ten years from 2001 to 2010. In this study 37
Commercial Banks were considered. Out of these 13 of them are foreign owned banks and 24
22
are owned by locals. The findings showed that bank specific factors significantly affect the
performance of Commercial Banks in Kenya, except for liquidity variable. Liquidity
management was positively related to ROA, ROE and NIM but the relationship was found to
be very weak.
Macharia (2013) sought to examine the relationship between the profitability and liquidity of
Commercial Banks in Kenya. The population of the study was comprised all 43Commercial
Banks in Kenya operating in the years 2008 to 2012. The study involved secondary data
collection of the return on assets, to measure profitability and CBK liquidity ratio and current
ratio to measure liquidity during a specific year. The study used descriptive statistics and
regression analysis to establish the relationship between the study variables. The study found
out that there is a positive relationship between profitability and liquidity of Commercial
Banks in Kenya; however, the coefficients from the study were found to be not significant.
Mwangi (2014) studied the effect of liquidity risk management on financial performance of
Commercial Banks in Kenya. The study involved all the 43 Commercial Banks in Kenya
analyzed for a period from 2010-2013. The results of the research showed that liquidity risk
management has a significant negative relationship with financial performance of
Commercial Banks. The study also concluded that holding more liquid assets as compared to
total assets would lead to lower returns to Commercial Banks in Kenya whereas holding more
liquid assets as compared to total deposits would lead to lower returns to Commercial Banks
in Kenya.
23
2.5 Summary of Literature Review
The theoretical review has examined theories that explain the impact of liquidity on
profitability. These theories hold certain assumptions constant and or with certainty. Baumol
model does not allow for cash flow fluctuations as it assumes cash inflows are certain and
regular and cash disbursements are steady and predictable. This is never the case in practice.
Miller – Orr model assumes that there is a set upper and lower limit of cash in a company, and
that the company reacts to restore the cash within these limits. However, conditions that are
beyond the control of the company may arise and this would lead to cash flows operating
beyond the aforementioned limits.
The studies have analyzed internal determinants of profitability. These factors include
liquidity, solvency, asset quality, bank size and growth. The results of their effect on
profitability have been mixed. For instance, Dang (2011) found out that adequate level of
liquidity is positively related with bank profitability whereas Molyneux et al, (1992) and Guru
et al., (1999) concluded that there was a negative relationship between the level of liquidity
and profitability.
The review of empirical studies both in Kenya and internationally have had mixed
conclusions as to how liquidity affects profitability. For example, Macharia (2013) found a
positive relationship between liquidity and profitability of banks in Kenya, Lartey et al.,
(2013) concluded that there was a very weak positive relationship between the liquidity and
the profitability of the listed banks in Ghana and Abera (2012) opined that the impact of
24
Ethiopian banks‟ liquidity on their performance remains ambiguous and further research is
required.
It is also clear from the literature review that no exhaustive study has been undertaken in
Kenya and the East Africa Region on how liquidity and solvency affect profitability of
Commercial Banks. Whereas the Region was not adversely affected by the 2007 financial
crisis, the banking sector experienced a liquidity crisis in 2011/2012. It would be interesting
to examine how liquidity and solvency, the heavenly twins of banking, Goodhart, (2008),
impact on profitability of banks.
25
CHAPTER THREE: RESEARCH METHODOLOGY
3.1 Introduction
This chapter presents the research methodology that was adopted in this study. The chapter
describes the research design, the population, data collection process, and data analysis model
and techniques adopted for the study.
3.2 Research Design
Cooper and Schindler (2005), observes that a design is a plan for selecting the sources and
types of information used to answer the research questions. The study used a descriptive
research design. According to Monsen and Van Horn, (2008), a descriptive study is one in
which information is collected without changing the environment i.e., nothing is manipulated.
This design was selected because the study seeks to determine the effect of liquidity and
solvency on profitability of Commercial Banks. Following Monsen and Van Horn (2008): “a
descriptive research can be used to propose an association.‟‟ The present study has proposed
an association between the two variables.
3.3 Population
The population of this study comprised the entire population of all the 43 Commercial Banks
in Kenya (Appendix 1). Since the number of Commercial Banks in Kenya is not large, all the
43 Commercial Banks formed the sample. Thus, this was a census study of all the
Commercial Banks in Kenya.
26
3.4 Data Collection
Data collection is gathering evidence in order to gain new insights about a situation and
answer the question that necessitated study. The study used secondary data. To ensure that the
study elements are complete and consistent, the researcher collected data for the Commercial
Banks that were in operation from 2010 to 2014. The five (5) year period was considered
adequate to provide the data that is in the analysis and this in line with past similar studies that
include Wambu (2013) and Mwangi (2014) which resulted in reliable results. Liquidity data
was deduced by looking at the current assets and current liabilities sections of the audited
financial statements (AFS). Solvency data was gathered from the AFS by looking at the
capital structure section. Liquidity and solvency ratios were then calculated as defined in
Table 3.1. Profitability data was also gathered from the annual reports by looking at the net
income section of the AFS of the Commercial Banks.
3.5 Data Analysis
Data analysis is a process of analysing all the information and evaluating the relevant
information that can be helpful in better decision making, Sivia and Skilling (2006). To
determine the effect of liquidity and solvency on profitability of the Commercial Banks two
types of data analysis techniques were used, i.e., descriptive and quantitative.
27
3.5.1 The Analytical Model
In this study, regression technique and correlation were used to establish the effect of liquidity
and solvency on profitability of Commercial Banks in Kenya.
The estimated regression model that was applied is as below:
Y = α + β1X1 + β2X2 + β3X3 + β4X4 + β5X5 +ε
Where:
Y=Profitability as measured by Return on Assets
α= Intercept
β= Coefficients of the variables
X1=Liquidity
X2=Solvency
X3=Asset Quality
X4=Bank Size
X5=Growth
ε= Random error term
The model also controlled for the effects of the industry and the year. These variables are
defined in Table 3.1.
28
3.5.2 Operationalization of the Variables
The table below discusses how the aforementioned variables can be operationalized.
Table 3.1: Operationalisation of variables
Variable Definition Measurement
Scale
Y Profitability as measured by Return on Assets (ROA) – (Net income
divided by Average total assets). Rivard and Thomas (1997) opined
that bank profitability is best measured by ROA because ROA can‟t
be distorted by high equity multiplier.
Ratio
X1 Liquidity as measured by current ratio (current assets divided by
current liabilities).
Ratio
X2 Solvency as measured by the ratio of debt to equity. Ratio
X3 Asset quality as measured by non -performing loans divided by gross
loans and advances.
Ratio
X4 Bank size as measured by natural log of the bank‟s total assets. Ratio
X5 Growth as measured by percentage of increase in revenue. Ratio
Source: Researcher
3.5.3 Tests of Significance
A correlation and a multiple regression analysis were carried out. A correlation matrix was
used to show the interrelationships within the variables under study. This helped show any
serial correlations. Analysis of Variance (ANOVA) and F-Test were used to show the fitness
of the model under study. The coefficients show how each of the variables influence
profitability.
29
The results of significance were interpreted at 5% level of significance. Adjusted R squared
was used to determine the variation in the dependent variable due to changes in the
independent variables. The p-values were interpreted.
30
CHAPTER FOUR: DATA ANALYSIS, FINDINGS AND
INTERPRETATIONS
4.1 Introduction
This chapter presents the results of the study. The chapter is organised as follows: the next
section presents the findings where the descriptive results are presented followed by the
correlation results and finally the regression results. The last section is the discussion of
findings.
4.2 Descriptive Statistics
The descriptive results in Table 4.1 present the number of observations, the mean scores and
the standard deviation. The results show that the analysis was based on data from 42 banks as
one bank, Dubai Bank Kenya Ltd., was dropped for lack of data for the entire period of study.
The mean ROA was 0.02, the mean of liquidity was 1.16 and that of solvency was 0.32. The
asset quality had a mean of 0.07 and the mean bank size, measured as the natural logarithm of
total assets, was 17.1. Finally, the growth as measured by the percentage increase in revenues
was 19%.
Table 4.1: Descriptive statistics
Mean Std. Deviation N
Return on Assets .02031 .019569 42
Liquidity 1.16190 .192484 42
Solvency .31869 .549389 42
Asset Quality .07321 .062780 42
Bank Size 17.10593 1.231949 42
Growth .19186 .186669 42
Source: Research findings
31
4.3 Inferential Statistics
Inferential statistics is concerned with making predictions or inferences about a population
from observations and analyses of a sample. Thus, inferential statistics attempts to generalize
the results of descriptive statistics to a larger population of interest. The study has applied
correlation analysis, regression analysis and ANOVA to make inferences about the population
of the Commercial Banks in Kenya.
4.3.1 Correlation Analysis
Table 4.2 shows the correlation results. As the correlation matrix shows, there were very low
correlations among the independent variables used in the study. These low correlations
suggest the absence of serial correlation in the dataset and, therefore, the variables can be
entered in the regression model for analysis as they are.
Table 4.2: Correlation matrix
ROA LIQ SOLV ASSET SIZE
Liquidity Pearson Correlation -.286 1
Sig. (2-tailed) .067
N 42 42
Solvency Pearson Correlation -.197 -.142 1
Sig. (2-tailed) .210 .371
N 42 42 42
Asset Quality Pearson Correlation -.321* .371
* .067 1
Sig. (2-tailed) .038 .016 .672
N 42 42 42 42
Bank Size Pearson Correlation .574**
-.331* .081 -.458
** 1
Sig. (2-tailed) .000 .032 .612 .002
N 42 42 42 42 42
32
Growth Pearson Correlation .018 .330* -.074 .200 -.139
Sig. (2-tailed) .912 .033 .644 .204 .379
N 42 42 42 42 42
*. Correlation is significant at the 0.05 level (2-tailed).
**. Correlation is significant at the 0.01 level (2-tailed).
Source: Research findings
4.3.2 Regression Analysis
Table 4.3 presented the summary model for the regression analysis. The study found that the
model explained 42.4% of the variance in bank performance as shown by the R2. The Durbin-
Watson value of 1.675 is closer to 2 and, therefore, shows that there was very low
autocorrelation in the model.
Table 4.3: Model summary
R R Square Adjusted R Square Std. Error of the Estimate Durbin-Watson
.651a .424 .344 .015853 1.675
a. Predictors: (Constant), Growth, Solvency, Bank Size, Liquidity, Asset Quality
b. Dependent Variable: Return on Assets
Source: Research findings
4.3.3 Analysis of Variance
Table 4.4 shows the analysis of variance results. The F value of 5.295 was significant at 1%
confidence level. Thus, the regression model used in the study was significant. This suggests
that at least one of the independent variables used in the study was significant.
33
Table 4.4: ANOVA
Sum of Squares df Mean Square F Sig.
Regression .007 5 .001 5.295 .001b
Residual .009 36 .000
Total .016 41
a. Dependent Variable: Return on Assets
b. Predictors: (Constant), Growth, Solvency, Bank Size, Liquidity, Asset Quality
Source: Research findings
Table 4.5 presents the coefficient results of the regression analysis. The results show that
liquidity had a negative but insignificant effect on the performance of banks in Kenya, p >
.05. The results also show that solvency had a negative but insignificant effect on bank
performance, p > .05. The study found that asset quality had a negative but insignificant effect
on bank performance in Kenya, p > .05. Bank size was found to have a positive and
significant effect on bank performance, p < .05 while growth has a positive but insignificant
effect on bank performance in Kenya, p > .05.
Table 4.5: Coefficients
Model
Unstandardized Coefficients
Standardized
Coefficients
t Sig. B Std. Error Beta
(Constant) -.107 .047 -2.292 .028
Liquidity -.018 .015 -.181 -1.240 .223
Solvency -.009 .005 -.256 -1.978 .056
Asset Quality -.004 .047 -.013 -.088 .930
Bank Size .009 .002 .548 3.760 .001
Growth .014 .014 .137 1.021 .314
a. Dependent Variable: Return on Assets
Source: Research findings
34
4.4 Interpretation of the Findings
The study sought to examine the effect of liquidity and solvency on bank performance. The
study found that liquidity had a negative but insignificant effect on the performance of banks
in Kenya (β= -0.018, p = 0.223). Thus, liquidity did not affect the performance of banks in
Kenya. This can be attributed to the fact that except for one Commercial Bank, most of the
banks had very low liquidities (less than 2) and thus the lower liquidities could not influence
the level of performance. The results are consistent with Lartey et al., (2013) who found a
weak relationship between liquidity and performance of Commercial Banks. The evidence,
therefore, suggests that the liquidity levels of banks in Kenya have a weak effect on the
performance of banks.
The study also found that solvency had a negative but marginally insignificant effect on the
performance of banks in Kenya (β= -0.009, p = 0.056). This shows that at 5% level of
significance, there is a weak evidence that solvency of banks affect their performance. This is
consistent with Kamoyo (2006) who found an insignificant relationship between solvency and
profitability of banks in Kenya. There is little evidence, therefore, that solvency affects the
performance of banks in Kenya.
The study also examined the effect of asset quality on the performance of banks in Kenya.
The results showed that asset quality has a negative but insignificant effect on the bank
performance (β= -0.004, p = 0.93). This is inconsistent with Sangmi and Nazir (2010) who
found a negative and significant relationship between asset quality and the performance of
35
Commercial Banks. This confirms that in Kenya, bank performance is not influenced by the
level of asset quality.
The study also examined how bank size affects the performance of banks in Kenya. The
results showed a positive and significant effect of bank size on bank performance (β= 0.009, p
= 0.001). This is consistent with Goddard et al., (2004) who found a positive and significant
relationship between bank size and profitability of banks. This confirms that bank
performance is influenced by the size of the bank in Kenya as larger banks perform better
than smaller banks.
Finally, the study examined the effect of bank growth on their performance. The results
showed a positive but insignificant effect of bank growth on their performance (β= 0.014, p =
0.314). This is consistent with Goddard, et al., (2004) who noted that the growth in bank size
could positively influence bank performance. This shows that bank performance in Kenya is
not influenced by the growth in revenues.
36
CHAPTER FIVE: SUMMARY, CONCLUSION AND
RECOMMENDATIONS
5.1 Introduction
This chapter presents the summary of the study, the conclusions, the recommendations for
policy and practice and suggestions for further research.
5.2 Summary
The study examined the effect of liquidity and solvency on the profitability of Commercial
Banks in Kenya. The study used a descriptive research design. The population of this study
comprised the entire population of all the 43 Commercial Banks in Kenya (Appendix 1).
Since the number of Commercial Banks in Kenya is not large, all the 43 Commercial Banks
formed the sample. However, one bank, the Dubai Bank Kenya Ltd., did not have the data for
all the years and was, therefore, deleted from the final sample. Five year secondary data was
collected from 2010 to 2014 for the banks from their annual reports. Data was analysed using
descriptive, correlation and regression analyses.
The descriptive results showed that the mean of ROA was 0.02, the mean of liquidity was
1.16 and that of solvency was 0.32. The asset quality had a mean of 0.07 and the mean bank
size, measured as the natural logarithm of total assets, was 17.1. The results also showed that
the average growth in revenues was 19%. The correlation analysis revealed that there were
low correlations among the independent variables. The regression results showed that the
37
model explained 42.4% of the variance in bank performance. The ANOVA results showed
that the model was statistically significant at 1% level of significance.
The study found that both liquidity and solvency had negative but insignificant effects on the
performance of banks in Kenya, p > .05. Further, the study found that asset quality had a
negative but insignificant effect on bank performance while growth had a positive but
insignificant effect on the bank performance in Kenya, p > .05. The results showed that bank
size had a positive and significant effect on bank performance, p < .05.
5.3 Conclusion
The study sought to examine the effect of liquidity on the performance of Commercial Banks
in Kenya. The study found that liquidity had a negative but insignificant effect on the
financial performance of Commercial Banks (β= -0.018, p = 0.223). The study, therefore,
concludes that the performance of Commercial Banks in Kenya is not influenced by the
liquidity levels.
The study examined the effect of solvency on the performance of Commercial Banks in
Kenya. The results showed that solvency had a negative but insignificant effect on the
performance of banks (β= -0.009, p = 0.056). It is concluded that bank performance in Kenya
is not influenced by the solvency levels in banks.
The study also examined the effect of asset quality on the financial performance of
Commercial Banks in Kenya. The results revealed that there was a negative but insignificant
38
effect of asset quality on the financial performance of banks (β= -0.004, p = 0.93). This leads
to the conclusion that bank performance is not influenced by the asset quality of banks.
The study further examined the effect of bank size on the performance of Commercial Banks
in Kenya. The study found that bank size had a positive and significant effect on the
performance of banks in Kenya (β= 0.009, p = 0.001). The study thus concludes that bank size
influences the financial performance of Commercial Banks in Kenya.
The study also examined the effect of growth on the performance of Commercial Banks in
Kenya. The results showed that the growth of banks had a positive but insignificant effect on
the performance of banks (β= 0.014, p = 0.314). This leads to the conclusion that the
performance of Commercial Banks in Kenya is not influenced by the growth of banks.
5.4 Recommendations for Policy and Practice
The study makes a number of recommendations. First, the study recommends that the
management of Commercial Banks in Kenya should take note of the fact that while the
liquidity and solvency levels of banks were not found to influence bank performance, it is
important to keep them at manageable levels in relation to the industry.
The study also recommends that bank managers should take note of the fact that the size of
the banks influences their performance. As such, Commercial Banks should strive to have
higher asset base in the industry in order to record better performance in terms of profitability.
39
The study further recommends that since growth in bank revenues may have a positive impact
on the performance of banks in Kenya, banks should focus on improving their revenue
sources in order to record better performance. As such, diversification of revenue sources
would be key.
5.5 Limitations of the Study
The study used OLS regression analysis on the aggregate data for the banks. This masks the
individual year effects on the model. A panel analysis would have been more preferable in
this case but due to certain data limitations, this was not possible. However, the OLS
regression analysis still did the task as was envisaged in the methodology.
The study also used liquidity and solvency as the main determinants of bank performance.
Together with other control variables, they accounted for only 42% of the variance in
performance. Thus, a number of variables were not examined in this study limiting the
performance determinants to two main predictor variables.
The study focused on Commercial Banks in Kenya. This means that the results are limited to
Kenya and may not be applicable to other countries with different operating environments. The
uniqueness of the operating environment may hinder application of these results in other countries
where the environment is different. Further, this study focused on commercial banks alone.
Thus, the results of this study are limited to the Commercial Banks examined in thus study.
Any attempt to apply the findings to other financial institutions other than commercial banks
should therefore be approached with care.
40
The study also used annual data in performing the analysis. While this was done due to
availability of annual data on most of the banks, it would have been prudent to use quarterly
data in order to increase the number of observations and, therefore, the predictive ability of
the model and its accuracy.
5.6 Suggestions for Further Research
There is need for more research in this area. More specifically, the study suggests that more
studies should focus on how both solvency and liquidity can influence bank performance
using a longer period of time, probably ten years, and using panel data methodologies to
examine this relationship.
The study also recommends that more studies be done to examine the determinants of bank
performance in Kenya. While this study attempted to examine this, it focused on liquidity,
solvency, asset quality, size and growth which only accounted for 42% of the variance in
performance. More variables, therefore, need to be examined.
This study suggests that a cross border study involving other countries should be carried out in
order to determine the impact of different economic and operating factors on the effect of liquidity
and solvency on the performance of Commercial Banks. In addition, future studies should also
perform an analysis of the effect of these variables on the performance of financial institutions
other than Commercial Banks. This will help provide results that can be generalised to all the
financial institutions in Kenya.
41
Further studies are also required in this area using quarterly data. This way, more observations
will be made and the model is more likely to provide better estimates than when the annual
data is used. This is also important because commercial banks report quarterly and thus such
an analysis will be more relevant to the banks.
42
REFERENCES
Abera, A. (2012). Factors affecting profitability: An empirical study on Ethiopian banking
industry, Unpublished Masters’ Thesis, Addis Ababa University. Available at:
http://etd.aau.edu.et/dspace/bitstream/123456789/4146/1/Factors%20Affecting%20Prof
itbility.pdf.
Akhavein, J., Berger N. and Humphrey D. (1997): The effects of megamergers on efficiency
and prices: evidence from a bank profit function. Review of Industrial Organization, 12,
95-139.
Alexiou, C. & Sofoklis, V. (2009). Determinants of bank profitability: Evidence from the
Greek banking sector. Economic annals, 54 (182) 93-118.
Athanasoglou, P. P., Brissimis, S. N. & Delis, M. D. (2007). Bank specific, industry specific
and macroeconomic determinants of bank profitability. Journal of International
Financial Markets, Institutions and Money, 18(2), 121-136.
Bank Supervision Annual Report (2013). Central Bank of Kenya.
Basel Committee on Banking Supervision (2010). Basel III: International framework for
liquidity risk measurement, standards and monitoring, Bank for International
Settlements, December 2010.
Basel Committee on Banking Supervision (2008). Principles for the management and
supervision of liquidity risk, Bank for International Settlements, September 2010.
Baumol, W. J. (1952). The transactions demand for cash: Inventory theoretic approach. The
Quarterly Journal of Economics.66 (4-11) 545-556.
Bikker, J.A., Haaf, K. (2002). Competition, concentration and their relationship: An empirical
analysis of the banking industry. Journal of Banking and Finance 26, 2191-2214.
Bordeleau, É. &Graham C. (2010). “The impact of liquidity on bank profitability‟‟, Bank of
Canada, Working Paper No. 2010-38.
Bourke, P. (1989). Concentration and other determinants of bank profitability in Europe.
Journal of Banking and Finance, 13(1), 65-80.
Brigham, E. and Houston, J.(2007). Fundamentals of Financial Management. (10th
ed.), Mason:
Thomson Publishing Limited.
Central Bank of Kenya (2013). Banking sector prudential guidelines 2013.
www.centralbank.go.ke
43
Cooper, D. & Schindler, P. (2005). Business research methods. Tata: McGraw-hill Edition.
Dang, U. (2011).The CAMEL rating system in banking supervision: a case study of Arcada
University of Applied Sciences, International Business.
Demirguc-Kunt, A. &Huizinga, H. (1999). Determinants of commercial bank interest margin
and profitability: some international evidence. The World Bank Economic Reviews.
13(2), 54-60.
Gilbert, R. (1984). “Bank market structure and competition – a survey”, Journal of Money
Credit and Banking, pp.45-617.
Gitman, L.J. (1997). Principles of Managerial Finance (7th
ed.). New York: Harper Collins
College Publishers, 684-710.
Goddard, J., Molyneux, P., and Wilson, J.O.S., (2004b). Dynamics of growth and profitability
in banking. Journal of Money, Credit and Banking, 36, 1069-1090.
Goodhart, C. (2008). The background to the 2007 financial crisis. Available at:
http://ideas.repec.org/a/kap/iecepo/v4y2008i4p331-346.html.
Guru, B.K., Staunton, J., & Balashanmugam, B. (1999). Determinants of Commercial Bank
profitability in Malaysia. Paper presented at the proceedings of the 12th Annual
Australian Finance and Banking Conference, Sydney, Australia. December 16–17,
1999.
Haslem, J.A. (1968). “A statistical analysis of the relative profitability of Commercial
Banks‟‟. Journal of Finance, 23, 167-176.
Husni Ali Khrawish (2011): Determinants of Commercial Banks performance: Evidence from
Jordan. Journal of Finance and Economics, pp. 149-158.
Kamoyo, E. M. (2006). Determinants of liquidity of Commercial Banks in Kenya,
Unpublished MBA research project, The University of Nairobi.
Keynes, J. (1936). The General theory of employment, interest and money, United Kingdom:
Palgrave Macmillan.
Kiganda, O, E (2014). Effect of macroeconomic factors on Commercial Banks profitability in
Kenya: Case of Equity Bank Limited. Journal of Economics and Sustainable
Development ISSN 2222-1700 (Paper) ISSN 2222-2855 (Online) 5(.2), 2014.
44
Kithii, J.N., (2008).The relationship between working capital management and profitability of
listed companies in the Nairobi Stock Exchange, Unpublished MBA Management
Research Paper, The University of Nairobi.
Konadu, J.S. (2009). Liquidity and Profitability: Empirical evidence from banks in Ghana.
Unpublished Masters’ Thesis, Kwame Nkrumah University of Science and
Technology.
Kosmidou K. Pasiouras F. Tanna S. (2005). Determinants of profitability of domestic UK
Commercial Banks: Panel evidence from the period 1995-2002. Available at:
http://repec.org/mmfc05/paper45.pdf.
Kosmidou, K., 2008. The determinants of banks‟ profits in Greece during the Period of EU
financial integration. Journal of Managerial Finance (2)146-159.
Lartey, V., Antwi, S., & Boadi, E. (2013). The relationship between liquidity and profitability
of listed banks in Ghana. International Journal of Business and Social Science, 4(3),
48–56. Available at: http://ijbssnet.com/journals.
Li, Y. (2007). Determinants of banks‟ profitability and its implication on risk management
practices: Panel evidence from the UK in the period 1999-2006, (Doctoral
dissertation). United Kingdom: The University of Nottingham.
Longworth, D. (2010): “Bank of Canada Liquidity Facilities: Past, Present, and Future”,
Remarks by David Longworth at the C.D. Howe Institute, Toronto, 17 February 2010.
Mahshid S. (2013).The impact of liquidity asset on Iranian banks profitability international
conference on management, behavioral sciences and Economics issues. Malaysia:
Penang.
Matz, L. (2011). Liquidity risk measurement and management. United States of America:
Xlibris Corporation.
Mehdi, F.& Mohammed, V. (2014). Liquidity and solvency in the international banking
regulation. Munich, Germany: The Clute Institute International Academic Conference.
Journal of Finance and Banking.
Miller, M. & Orr, D. (1966). A model of the demand for money by firms. Quarterly Journal
of Economics, 80, 413-435.
Molyneux, P. & Thorton.J. (1992). The determinants of European bank profitability. Journal
of Banking and Finance, 16 (6), 1173-1178.
45
Monsen, E.R. &Van Horn, L. (2008). Research successful approaches (3rd ed.), Chicago:
American Dietetic Association.
Munther,N,Warrad,L,& Omari,R.(2013). The Impact of liquidity on Jordanian banks
profitability through return on assets. Interdisciplinary Journal of Contemporary
Research in Business.
Myers, Stewart C.; Majluf, Nicholas S. (1984). Corporate financing and investment decisions
when firms have information that investors do not have. Journal of Financial
Economics. pp.187-221.
Mwangi, F. (2014). The effect of liquidity risk management on the financial performance of
Commercial Banks in Kenya. Unpublished MSC in Finance research project, The
University of Nairobi.
Naceur, S. B. (2003). Determinants of the Tunisian banking industry profitability: Panel
evidence. Journal of Frontiers in Finance and Economics, 5(1): 106-130.
Nimer, M., Warrad, L. & Omari, R. (2013).The impact of liquidity on Jordanian banks
profitability through return on assets. International Journal of Contemporary
Research in Business, 5(7), 70-76.
Olweny, T., & Shipho, T. (2011). Effects of banking sectoral factors on the profitablity of
Commercial Banks in Kenya. Economics and Finance Review, 1 (5), 1-30.
Ongore, V.O. & Kusa, G.B. (2013). Determinants of financial performance of Commercial
Banks in Kenya. International Journal of Economics and Financial Issues, 3(1), 237-
252.
Pasiouras, F., Kosmidou, K., 2007. Factors influencing the profitability of domestic and
foreign Commercial Banks in the European Union. Research in International Business
and Finance, 21, 222–237.
Perry P. (1992). „„Do banks gain or lose from inflation?‟‟, Journal of Retail Banking, 14, 25-
40.
Rivard, R. J. and Thomas, C. R. (1997). The effect of interstate banking on large bank holding
company profitability and risk. Journal of Economics and Business 49(1), 61-76.
Sangmi, M. & Nazir, T. (2010). „‟Analysing financial performance of Commercial Banks in
India: Application of CAMEL Model,‟‟ Pakistan Journal of Commerce and Social
Science 4(1), 40-55.
46
Short, B., 1979. The relationship between Commercial Bank profit rates and banking
concentration in Canada, Western Europe and Japan. Journal of Banking and Finance.
Sivia, D.S &Skilling,S. (2006). Data analysis: A Bayesian tutorial (2nd ed.) Oxford: Oxford
University Press.
Smirlock, M. (1985). „‟Evidence of the (Non) relationship between concentration and
profitability in banking‟‟, Journal of Money, Credit and Banking, 1, pp. 69-83.
Sufian, F. (2009). “Determinants of bank efficiency during unstable macroeconomic
environment: Empirical evidence from Malaysia”, Journal of Research in
International Business and Finance, 23, 54-77.
The Economic Times (5 August 2014).
Wambu, T. (2013). The relationship between profitability and liquidity of Commercial Banks
in Kenya, Unpublished MBA research project, The University of Nairobi.
Waqas, B. & Mobeen, R. (2014). Impact of liquidity and solvency on profitability of the
chemical sector of Pakistan. Journal in Economics management. Available at:
http://emi.mvso.cz.
47
APPENDICES
Appendix I: List of Commercial Banks in Kenya as at 31 December 2014
1
1. African Banking Corporation Ltd.
2
22. Fina Bank Ltd.
2
2. Bank of Africa (K) Ltd.
2
23. First Community Bank Ltd.
3
3. Bank of Baroda (K) Ltd.
2
24. Giro Commercial Bank Ltd.
4
4. Bank of India
2
25. Guardian Bank Ltd.
5
5. Barclays Bank of Kenya Ltd.
2
26. Gulf African Bank (K) Ltd.
6
6. CFC Stanbic Bank Ltd.
2
27. Habib Bank A.G Zurich
7
7. Charterhouse Bank Ltd.
2
28. Habib Bank Ltd .
8. Chase Bank (K) Ltd.
2
29. I&M Bank Ltd.
9
9. Citibank N.A.
3
30. Imperial Bank Ltd.
1
10. Commercial Bank of Africa Ltd.
3
31. Jamii Bora Bank Ltd.
11. Consolidated Bank of Kenya Ltd.
3
32. Kenya Commercial Bank Ltd.
12. Co-operative Bank of Kenya Ltd.
3
33. K-Rep Bank Ltd.
13. Credit Bank Ltd.
3
34. Middle East Bank (K) Ltd.
14. Development Bank of Kenya Ltd.
3
35. National Bank of Kenya Ltd.
15. Diamond Trust Bank (K) Ltd.
3
36. National Industrial Credit Bank Ltd.
16. Dubai Bank Kenya Ltd.
3
37. Oriental Commercial Bank Ltd.
17. Ecobank Kenya Ltd.
3
38. Paramount Universal Bank Ltd.
18. Equatorial Commercial Bank Ltd.
3
39. Prime Bank Ltd.
19. Equity Bank Ltd.
4
40. Standard Chartered Bank (K) Ltd.
20. Family Bank Ltd.
4
41. Trans-National Bank Ltd.
21. Fidelity Commercial Bank Ltd.
4
42. UBA Kenya Bank Ltd.
4
43. Victoria Commercial Bank Ltd.
Source: Central Bank of Kenya
48
Appendix II: Final Research Data for Analysis
Bank ROA Liquidity Solvency Asset quality Size Growth
KCB 0.038 1.220 0.149 0.063 19.511 0.209
Barclays 0.046 1.199 0.156 0.112 19.063 -0.004
Co op 0.033 1.165 0.179 0.070 19.122 0.186
Equity 0.055 1.240 0.503 0.035 19.124 0.188
Stan Chart 0.041 1.182 0.027 0.026 19.044 0.152
CfC 0.022 1.135 0.515 0.023 18.774 0.163
Citibank 0.041 1.287 0.027 0.009 18.080 0.149
I&M 0.038 1.224 0.326 0.015 18.340 0.226
NBK 0.017 1.164 0.001 0.075 18.189 0.073
NIC 0.031 1.176 0.219 0.035 18.330 0.213
DTB 0.030 1.184 0.249 0.013 18.347 0.205
CBA 0.026 1.129 0.082 0.043 18.453 0.059
BOB 0.034 1.135 0.000 0.029 17.613 0.114
BOI 0.028 1.188 0.000 0.015 17.077 0.336
BOA 0.010 1.135 0.311 0.029 17.675 0.074
Prime 0.025 1.133 0.000 0.027 17.555 0.212
Imperial 0.043 1.162 0.000 0.049 17.325 0.184
Family 0.021 1.175 0.209 0.092 17.333 0.254
Ecobank -0.010 1.134 2.335 0.106 17.313 0.189
Chase 0.019 1.103 0.540 0.025 17.733 0.480
Housing 0.017 1.149 1.743 0.071 17.515 0.163
Trans 0.022 1.305 0.000 0.074 15.879 0.079
ABC 0.024 1.150 0.246 0.041 16.585 0.092
Giro 0.030 1.169 0.000 0.019 16.342 -0.008
DBK 0.011 1.157 1.565 0.143 16.412 0.080
Fina 0.014 1.196 1.713 0.048 16.798 0.106
K-Rep 0.021 1.176 0.663 0.115 16.189 0.145
Gulf 0.014 1.156 0.018 0.049 16.453 0.220
Victoria 0.031 1.217 0.315 0.000 16.147 0.888
Habib AG 0.019 1.189 0.094 0.026 16.101 0.129
Oriental 0.022 1.299 0.000 0.112 15.609 0.012
Guardian 0.015 1.131 0.000 0.089 16.207 0.178
Middle 0.026 1.282 0.000 0.215 15.461 0.079
Equatorial -0.011 1.085 0.187 0.146 16.436 0.225
Habib 0.034 1.235 0.000 0.040 15.764 0.190
Consolidated 0.002 1.114 0.929 0.141 16.516 -0.044
Paramount 0.015 1.202 0.000 0.290 15.705 0.779
Credit 0.005 1.212 0.000 0.107 15.660 0.052
Fidelity 0.018 1.113 0.000 0.076 16.276 0.123
Jamii Bora -0.007 2.312 0.084 0.227 15.224 0.582
First 0.006 1.111 0.000 0.112 16.110 0.273
UBA -0.063 1.427 0.000 0.043 15.059 0.053
Source: Audited Financial Statements of the Commercial Banks
49
Appendix III: Annual Ratios for variables I
ROA
Liquidity
Solvency
Bank 2014 2013 2012 2011 2010 2014 2013 2012 2011 2010 2014 2013 2012 2011 2010
KCB 0.042 0.038 0.036 0.035 0.040 1.237 1.239 1.211 1.190 1.224 0.184 0.209 0.163 0.189 0.000
Barclays 0.037 0.037 0.047 0.049 0.061 1.203 1.185 1.191 1.194 1.223 0.348 0.279 0.152 0.000 0.000
Co op 0.030 0.039 0.037 0.031 0.028 1.176 1.185 1.170 1.143 1.151 0.431 0.288 0.158 0.011 0.007
Equity 0.061 0.053 0.051 0.055 0.056 1.168 1.270 1.246 1.247 1.268 0.749 0.505 0.604 0.393 0.264
Stan Chart 0.047 0.042 0.041 0.036 0.038 1.222 1.195 1.186 1.143 1.165 0.135 0.000 0.000 0.000 0.000
CfC 0.032 0.029 0.023 0.014 0.014 1.184 1.151 1.157 1.078 1.103 0.000 0.981 0.323 0.614 0.658
Citibank 0.031 0.042 0.064 0.039 0.028 1.301 1.289 1.332 1.254 1.262 0.000 0.133 0.000 0.000 0.000
I&M 0.041 0.038 0.037 0.040 0.034 1.189 1.229 1.221 1.220 1.262 0.630 0.548 0.221 0.214 0.019
NBK 0.007 0.012 0.011 0.023 0.034 1.109 1.147 1.184 1.180 1.198 0.002 0.000 0.003 0.000 0.000
NIC 0.029 0.030 0.029 0.034 0.032 1.198 1.185 1.174 1.155 1.168 0.612 0.206 0.243 0.019 0.013
DTB 0.029 0.036 0.032 0.029 0.021 1.223 1.194 1.187 1.155 1.159 0.361 0.262 0.252 0.370 0.000
CBA 0.021 0.028 0.026 0.020 0.032 1.113 1.124 1.131 1.135 1.140 0.393 0.000 0.016 0.000 0.000
BOB 0.036 0.026 0.030 0.037 0.042 1.189 1.171 1.143 1.155 1.018 0.000 0.000 0.000 0.000 0.000
BOI 0.030 0.033 0.023 0.033 0.019 1.215 1.198 1.195 1.169 1.163 0.000 0.000 0.000 0.000 0.000
BOA 0.002 0.014 0.010 0.011 0.011 1.146 1.142 1.114 1.137 1.137 0.524 0.459 0.175 0.198 0.198
Prime 0.032 0.029 0.022 0.024 0.019 1.164 1.133 1.106 1.119 1.140 0.000 0.000 0.000 0.000 0.000
Imperial 0.036 0.043 0.041 0.047 0.046 1.152 1.153 1.152 1.168 1.185 0.000 0.000 0.000 0.000 0.000
Family 0.029 0.028 0.017 0.014 0.018 1.207 1.159 1.186 1.147 1.176 0.273 0.225 0.184 0.204 0.157
Ecobank -0.007 -0.024 -0.033 0.007 0.005 1.205 1.101 1.067 1.068 1.229 0.662 2.302 3.929 4.781 0.000
Chase 0.022 0.021 0.018 0.016 0.017 1.115 1.108 1.116 1.089 1.085 1.401 0.785 0.458 0.056 0.000
Housing 0.014 0.017 0.017 0.021 0.013 1.116 1.138 1.145 1.176 1.170 2.708 0.000 2.316 1.676 2.015
Trans 0.012 0.016 0.024 0.028 0.030 1.230 1.240 1.263 1.314 1.478 0.000 0.000 0.000 0.000 0.000
ABC 0.012 0.022 0.023 0.030 0.033 1.139 1.143 1.125 1.158 1.188 0.591 0.277 0.360 0.000 0.000
Giro 0.026 0.028 0.018 0.025 0.050 1.191 1.181 1.169 1.154 1.151 0.000 0.000 0.000 0.000 0.000
DBK 0.013 0.012 0.005 0.009 0.015 1.195 1.132 1.139 1.157 1.162 0.947 1.313 1.871 1.992 1.701
Fina 0.016 0.013 0.017 0.015 0.009 1.277 1.312 1.171 1.117 1.105 0.027 0.034 0.078 8.110 0.316
K-Rep 0.033 0.027 0.021 0.019 0.007 1.182 1.165 1.190 1.167 1.178 0.408 0.578 0.625 0.920 0.786
Gulf 0.020 0.018 0.018 0.007 0.008 1.189 1.201 1.130 1.114 1.146 0.000 0.089 0.000 0.000 0.000
Victoria 0.027 0.032 0.034 0.030 0.035 1.200 1.227 1.246 1.196 1.216 0.482 0.586 0.279 0.226 0.000
50
Habib AG 0.033 0.028 0.026 0.019 -0.011 1.227 1.201 1.187 1.172 1.159 0.000 0.332 0.076 0.000 0.061
Oriental 0.009 0.020 0.015 0.030 0.034 1.255 1.278 1.286 1.345 1.333 0.000 0.000 0.000 0.000 0.000
Guardian 0.018 0.021 0.013 0.013 0.009 1.137 1.132 1.116 1.137 1.134 0.000 0.000 0.000 0.000 0.000
Middle 0.012 0.012 0.008 0.065 0.035 1.262 1.256 1.237 1.311 1.343 0.000 0.000 0.000 0.000 0.000
Equatorial -0.020 0.004 -0.034 0.006 -0.010 1.075 1.097 1.054 1.103 1.095 0.346 0.146 0.277 0.166 0.000
Habib 0.034 0.039 0.041 0.028 0.028 1.259 1.260 1.238 1.221 1.198 0.000 0.000 0.000 0.000 0.000
Consolidated -0.019 -0.006 0.008 0.010 0.016 1.116 1.080 1.096 1.103 1.175 1.310 1.691 1.642 0.000 0.000
Paramount 0.021 0.011 0.015 0.021 0.009 1.153 1.181 1.186 1.277 1.216 0.000 0.000 0.000 0.000 0.000
Credit -0.010 0.007 0.011 0.009 0.007 1.149 1.204 1.225 1.216 1.264 0.000 0.000 0.000 0.000 0.000
Fidelity 0.013 0.017 0.008 0.018 0.033 1.116 1.124 1.112 1.104 1.108 0.000 0.000 0.000 0.000 0.000
Jamii Bora 0.002 0.013 0.015 -0.018 -0.045 1.310 1.473 2.510 3.812 2.456 0.130 0.053 0.057 0.052 0.128
First 0.001 0.012 0.024 0.008 -0.015 1.110 1.120 1.121 1.106 1.097 0.000 0.000 0.000 0.000 0.000
UBA -0.059 -0.073 -0.098 -0.047 -0.038 1.315 1.400 1.715 1.294 1.410 0.000 0.000 0.000 0.000 0.000
Source: Audited Financial Statements of the Commercial Banks
51
Appendix IV: Annual Ratios for variables II
Asset quality
Size
Growth
Bank 2014 2013 2012 2011 2010 2014 2013 2012 2011 2010 2014 2013 2012 2011 roa
KCB 0.046 0.068 0.056 0.052 0.093 19.748 19.594 19.533 19.459 19.223 0.152 0.090 0.107 0.488 0.038
Barclays 0.036 0.030 0.362 0.055 0.075 19.237 19.148 19.035 18.929 18.967 0.007 -0.021 0.065 -0.068 0.046
Co op 0.043 0.040 0.045 0.038 0.182 19.460 19.249 19.112 18.938 18.852 0.130 0.188 0.298 0.127 0.033
Equity 0.030 0.043 0.023 0.024 0.054 19.436 19.289 19.190 18.991 18.713 0.180 0.115 0.271 0.185 0.055
Stan Chart 0.072 0.024 0.015 0.007 0.013 19.221 19.212 19.091 18.916 18.778 0.084 0.134 0.284 0.106 0.041
CfC 0.034 0.026 0.016 0.013 0.026 18.959 18.956 18.709 18.758 18.490 0.041 0.132 0.314 0.166 0.022
Citibank 0.024 0.006 0.006 0.005 0.006 18.190 18.082 18.058 18.128 17.944 -0.034 -0.202 0.396 0.437 0.041
I&M 0.016 0.010 0.009 0.015 0.024 18.738 18.519 18.332 18.158 17.952 0.246 0.244 0.102 0.311 0.038
NBK 0.107 0.105 0.077 0.041 0.044 18.627 18.343 18.023 18.045 17.910 0.136 0.185 -0.031 0.000 0.017
NIC 0.034 0.041 0.031 0.032 0.036 18.736 18.542 18.438 18.114 17.819 0.149 0.213 0.259 0.229 0.031
DTB 0.011 0.013 0.014 0.011 0.018 18.766 18.553 18.364 18.165 17.886 0.131 0.195 0.301 0.191 0.030
CBA 0.034 0.034 0.038 0.049 0.062 18.985 18.643 18.425 18.238 17.974 0.055 0.198 0.262 -0.279 0.026
BOB 0.033 0.022 0.023 0.031 0.034 17.942 17.768 17.647 17.418 17.292 0.438 0.003 0.090 -0.074 0.034
BOI 0.006 0.010 0.016 0.023 0.022 17.353 17.240 17.029 16.966 16.795 0.043 0.708 -0.254 0.845 0.028
BOA 0.055 0.039 0.021 0.016 0.011 17.946 17.780 17.706 17.472 17.472 -0.073 0.341 0.229 -0.200 0.010
Prime 0.013 0.019 0.028 0.036 0.037 17.821 17.717 17.587 17.376 17.272 0.178 0.395 0.120 0.157 0.025
Imperial 0.060 0.053 0.041 0.044 0.046 17.852 17.577 17.359 17.059 16.777 0.057 0.312 0.152 0.216 0.043
Family 0.063 0.072 0.137 0.101 0.086 17.940 17.588 17.249 17.074 16.816 0.270 0.466 0.180 0.099 0.021
Ecobank 0.087 0.078 0.051 0.089 0.223 17.643 17.424 17.274 17.119 17.107 0.697 0.866 -0.653 -0.154 -0.010
Chase 0.042 0.026 0.016 0.018 0.024 18.489 18.154 17.709 17.413 16.900 0.428 0.525 0.538 0.430 0.019
Housing 0.088 0.086 0.069 0.053 0.061 17.918 17.660 17.521 17.280 17.194 0.156 0.272 0.010 0.213 0.017
Trans 0.074 0.116 0.045 0.052 0.082 16.142 16.083 15.990 15.802 15.376 0.054 -0.097 0.179 0.181 0.022
ABC 0.052 0.044 0.034 0.029 0.045 16.881 16.793 16.764 16.342 16.147 -0.008 0.124 0.144 0.108 0.024
Giro 0.023 0.041 0.012 0.005 0.013 16.529 16.427 16.323 16.288 16.141 0.128 0.221 -0.071 -0.308 0.030
DBK 0.134 0.124 0.147 0.179 0.131 16.646 16.562 16.412 16.260 16.181 0.099 0.567 -0.149 -0.197 0.011
Fina 0.022 0.025 0.038 0.062 0.093 17.312 17.060 16.658 16.499 16.463 0.223 0.202 0.085 -0.087 0.014
K-Rep 0.067 0.077 0.118 0.114 0.201 16.575 16.396 16.072 16.048 15.853 0.197 0.257 0.069 0.058 0.021
Gulf 0.065 0.058 0.034 0.064 0.023 16.799 16.591 16.423 16.374 16.077 0.201 0.128 0.361 0.192 0.014
Victoria 0.000 0.000 0.000 0.000 0.000 16.663 16.429 16.150 15.850 15.643 0.076 0.203 0.372 2.901 0.031
52
Habib AG 0.014 0.021 0.029 0.028 0.035 16.313 16.214 16.088 15.981 15.911 0.281 0.097 0.327 -0.189 0.019
Oriental 0.098 0.091 0.121 0.126 0.125 15.877 15.762 15.643 15.431 15.332 -0.114 0.322 -0.095 -0.065 0.022
Guardian 0.065 0.059 0.064 0.069 0.189 16.495 16.368 16.279 15.994 15.899 0.037 0.429 0.222 0.023 0.015
Middle 0.520 0.169 0.092 0.123 0.169 15.597 15.567 15.585 15.350 15.206 0.089 0.164 -0.607 0.671 0.026
Equatorial 0.251 0.119 0.074 0.072 0.216 16.624 16.560 16.462 16.375 16.157 -0.482 1.716 -0.509 0.174 -0.011
Habib 0.066 0.000 0.094 0.017 0.024 16.061 15.905 15.763 15.584 15.507 0.048 0.126 0.410 0.176 0.034
Consolidated 0.253 0.127 0.114 0.088 0.121 16.529 16.636 16.706 16.545 16.165 -0.386 0.121 -0.036 0.126 0.002
Paramount 0.066 0.103 0.300 0.374 0.607 16.158 15.899 15.797 15.369 15.302 0.633 0.047 0.042 2.393 0.015
Credit 0.082 0.058 0.093 0.109 0.194 15.998 15.805 15.673 15.501 15.326 -0.060 0.146 0.172 -0.049 0.005
Fidelity 0.062 0.081 0.103 0.040 0.093 16.620 16.363 16.281 16.194 15.921 -0.017 0.719 -0.262 0.053 0.018
Jamii Bora 0.083 0.066 0.111 0.523 0.351 16.389 15.763 15.062 14.543 14.362 0.475 0.801 1.446 -0.396 -0.007
First 0.151 0.070 0.141 0.128 0.073 16.542 16.241 16.114 15.983 15.669 0.098 -0.020 0.352 0.661 0.006
UBA 0.063 0.016 0.095 0.042 0.000 15.375 15.126 14.888 14.981 14.924 -0.078 1.029 -0.576 -0.161 -0.063
Source: Audited Financial Statements of the Commercial Banks
